Natural Language Processing Projects for Beginners

 🧠 1. Sentiment Analysis on Product Reviews


Objective: Classify customer reviews as positive, negative, or neutral.

DataCamp

+3

Your Excel Buddy

+3

Intellipaat

+3


Implementation Steps:


Collect a dataset of product reviews.


Preprocess the text (tokenization, removing stopwords).


Convert text to numerical features using TF-IDF or word embeddings.


Train a machine learning model (e.g., Logistic Regression, Naive Bayes).


Evaluate the model's performance using accuracy, precision, recall, and F1-score.

ProjectPro

+2

Intellipaat

+2

Intellipaat

+1


Tools: Python, NLTK, Scikit-learn, pandas


📰 2. Fake News Detection


Objective: Identify and classify fake news articles.

GeeksforGeeks

+2

GUVI

+2


Implementation Steps:


Gather a labeled dataset of news articles.


Preprocess the text data.


Extract features using TF-IDF or word embeddings.


Train a classification model (e.g., SVM, Random Forest).


Evaluate the model's performance.

LinkedIn

+2

DataCamp

+2

ProjectPro

Intellipaat


Tools: Python, Scikit-learn, pandas


📩 3. Spam SMS Classification


Objective: Classify SMS messages as spam or ham (non-spam).

GeeksforGeeks

+2

LinkedIn

+2


Implementation Steps:


Collect a dataset of SMS messages.


Preprocess the text data.


Convert text to numerical features.


Train a classification model.


Evaluate the model's performance.

LinkedIn

+1

Intellipaat

+1


Tools: Python, Scikit-learn, pandas


🧑‍💻 4. Named Entity Recognition (NER)


Objective: Identify and classify named entities (e.g., people, organizations, locations) in text.

LinkedIn

+1


Implementation Steps:


Obtain a labeled dataset with annotated entities.


Preprocess the text data.


Use an NER model (e.g., spaCy).


Evaluate the model's ability to correctly identify entities.

LinkedIn

+1


Tools: Python, spaCy


💬 5. Chatbot Development


Objective: Develop a simple chatbot that can answer predefined questions.

Analytics Insight

+2

Your Excel Buddy

+2


Implementation Steps:


Define a set of questions and answers.


Preprocess the questions.


Implement a matching algorithm (e.g., cosine similarity).


Develop a user interface for interaction.


Tools: Python, NLTK, Flask


🧾 6. Text Summarization


Objective: Generate a concise summary of a given text.


Implementation Steps:


Collect a dataset of documents and their summaries.


Preprocess the text data.


Implement an extractive summarization algorithm (e.g., TextRank).


Evaluate the quality of the generated summaries.

Intellipaat

+1


Tools: Python, NLTK, Gensim


🔠 7. Text Classification Using Bag of Words


Objective: Classify text documents into predefined categories.


Implementation Steps:


Collect a labeled dataset of text documents.


Preprocess the text data.


Convert text to numerical features using the Bag of Words model.


Train a classification model.


Evaluate the model's performance.

Your Excel Buddy

ProjectPro


Tools: Python, Scikit-learn, pandas

LinkedIn


🧪 8. Grammar and Spell Checker


Objective: Develop a tool that can detect and correct grammatical and spelling errors in text.


Implementation Steps:


Collect a dataset of text with grammatical and spelling errors.


Preprocess the text data.


Implement a grammar and spell checking algorithm.


Evaluate the tool's accuracy.


Tools: Python, LanguageTool, spaCy


📊 9. Sentiment Analysis on Social Media Posts


Objective: Analyze the sentiment of posts on social media platforms.


Implementation Steps:


Collect a dataset of social media posts.


Preprocess the text data.


Convert text to numerical features.


Train a sentiment analysis model.


Evaluate the model's performance.

ProjectPro

+3

Intellipaat

+3

Your Excel Buddy

+3


Tools: Python, Tweepy (for Twitter API), Scikit-learn


📚 10. Language Translation


Objective: Translate text from one language to another.


Implementation Steps:


Collect a parallel corpus of text in two languages.


Preprocess the text data.


Train a machine translation model (e.g., seq2seq).


Evaluate the model's translation quality.


Tools: Python, TensorFlow, Keras

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